Two-step procedure for classifying consumers in a L-structured data context

This paper proposes a method based on CLV (Clustering around Latent Variables; [Vigneau, E., & Qannari, E. M. (2003). Clustering of variables around latent components. Communications in Statistics Simulation and Computation, 12(4), 1131–1150] for identifying groups of consumers in L-shaped data structures. This kind of data structure is very common in consumer studies, when a panel of consumers is asked to assess overall liking of a certain number of products and the preference scores are then arranged in a two-way table, Y. External information both on products (physical–chemical characterisation or sensory profile) and consumers (socio-demographic background, purchasing behaviour or consumption habits) may be available respectively in a row-descriptor matrix, X and a column-descriptor matrix, Z. The aim is to automatically provide consumer segmentation, with all three matrices playing an active role in classification, obtaining groups which are as homogeneous as possible from every point of view: preferences, products and consumer characteristics. The proposed clustering method is compared with the procedure adopted in [Esposito Vinzi, V., Guinot, C., & Squillacciotti, S. (2007). Two-step PLS regression for L-structured data: An application in the cosmetics industry. Statistical Methods and Applications, 16(2), 263–278], in which double PLS regression is used to capture all the information contained in the L-structure and consumer classification is then carried out on the columns of the final component matrix. Furthermore, the two approaches are illustrated using data from a preference study on juices based on berry fruits. The hedonic ratings of 72 consumers for 25 fruit juice mixes have been explained with regard to the product’s chemical characterisation and socio-demographic information, purchasing behaviour and the consumption habits of consumers.